Overview

Dataset statistics

Number of variables11
Number of observations700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.3 KiB
Average record size in memory88.2 B

Variable types

Numeric10
Categorical1

Warnings

km is highly correlated with current priceHigh correlation
current price is highly correlated with kmHigh correlation
km is highly correlated with current priceHigh correlation
current price is highly correlated with kmHigh correlation
km is highly correlated with current priceHigh correlation
current price is highly correlated with kmHigh correlation
current price is highly correlated with kmHigh correlation
km is highly correlated with current priceHigh correlation
km has unique values Unique
current price has unique values Unique

Reproduction

Analysis started2022-02-28 11:48:55.770962
Analysis finished2022-02-28 11:49:02.118652
Duration6.35 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

on road old
Real number (ℝ≥0)

Distinct699
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean602474.9343
Minimum500265
Maximum699859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.149436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum500265
5-th percentile510373.2
Q1549582.25
median602081.5
Q3652267.25
95-th percentile691382.55
Maximum699859
Range199594
Interquartile range (IQR)102685

Descriptive statistics

Standard deviation57982.26794
Coefficient of variation (CV)0.09624013322
Kurtosis-1.20835805
Mean602474.9343
Median Absolute Deviation (MAD)50951
Skewness-0.07193789352
Sum421732454
Variance3361943395
MonotonicityNot monotonic
2022-02-28T03:49:02.201550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6716302
 
0.3%
5842961
 
0.1%
6662851
 
0.1%
5878121
 
0.1%
6178931
 
0.1%
6939861
 
0.1%
6753051
 
0.1%
6638771
 
0.1%
5633961
 
0.1%
5576991
 
0.1%
Other values (689)689
98.4%
ValueCountFrequency (%)
5002651
0.1%
5009211
0.1%
5015691
0.1%
5021311
0.1%
5022811
0.1%
5027941
0.1%
5031571
0.1%
5032551
0.1%
5033271
0.1%
5038561
0.1%
ValueCountFrequency (%)
6998591
0.1%
6997141
0.1%
6995701
0.1%
6990231
0.1%
6988481
0.1%
6987481
0.1%
6987191
0.1%
6986121
0.1%
6984931
0.1%
6984531
0.1%

on road now
Real number (ℝ≥0)

Distinct699
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean799688.7
Minimum700159
Maximum899797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.255998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum700159
5-th percentile710067.4
Q1750222.75
median799772
Q3849847.75
95-th percentile887507.1
Maximum899797
Range199638
Interquartile range (IQR)99625

Descriptive statistics

Standard deviation57652.08067
Coefficient of variation (CV)0.07209315409
Kurtosis-1.227771678
Mean799688.7
Median Absolute Deviation (MAD)49861.5
Skewness-0.03703531488
Sum559782090
Variance3323762406
MonotonicityNot monotonic
2022-02-28T03:49:02.307302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7402002
 
0.3%
7317861
 
0.1%
8916271
 
0.1%
7282351
 
0.1%
7273741
 
0.1%
8457491
 
0.1%
7127811
 
0.1%
7931451
 
0.1%
8799641
 
0.1%
7344801
 
0.1%
Other values (689)689
98.4%
ValueCountFrequency (%)
7001591
0.1%
7001771
0.1%
7004281
0.1%
7005361
0.1%
7007461
0.1%
7008851
0.1%
7011081
0.1%
7013391
0.1%
7023661
0.1%
7024421
0.1%
ValueCountFrequency (%)
8997971
0.1%
8997111
0.1%
8997021
0.1%
8990831
0.1%
8984761
0.1%
8984351
0.1%
8984111
0.1%
8983471
0.1%
8975661
0.1%
8973571
0.1%

years
Real number (ℝ≥0)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.66
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.351067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median5
Q36
95-th percentile7
Maximum7
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.728776888
Coefficient of variation (CV)0.3709821648
Kurtosis-1.289963265
Mean4.66
Median Absolute Deviation (MAD)2
Skewness-0.1115244232
Sum3262
Variance2.988669528
MonotonicityNot monotonic
2022-02-28T03:49:02.443770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7140
20.0%
6123
17.6%
5115
16.4%
3111
15.9%
4107
15.3%
2104
14.9%
ValueCountFrequency (%)
2104
14.9%
3111
15.9%
4107
15.3%
5115
16.4%
6123
17.6%
7140
20.0%
ValueCountFrequency (%)
7140
20.0%
6123
17.6%
5115
16.4%
4107
15.3%
3111
15.9%
2104
14.9%

km
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100295.7786
Minimum50324
Maximum149902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.485827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50324
5-th percentile55702.9
Q174576.75
median99671.5
Q3125116
95-th percentile145400.7
Maximum149902
Range99578
Interquartile range (IQR)50539.25

Descriptive statistics

Standard deviation28951.10959
Coefficient of variation (CV)0.2886573094
Kurtosis-1.213013331
Mean100295.7786
Median Absolute Deviation (MAD)25319.5
Skewness-0.01703491297
Sum70207045
Variance838166746.3
MonotonicityNot monotonic
2022-02-28T03:49:02.532694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1280711
 
0.1%
705971
 
0.1%
910301
 
0.1%
821911
 
0.1%
520861
 
0.1%
1256691
 
0.1%
563871
 
0.1%
1051391
 
0.1%
1201381
 
0.1%
1230281
 
0.1%
Other values (690)690
98.6%
ValueCountFrequency (%)
503241
0.1%
504601
0.1%
506481
0.1%
508151
0.1%
508881
0.1%
510321
0.1%
515541
0.1%
516931
0.1%
520861
0.1%
522131
0.1%
ValueCountFrequency (%)
1499021
0.1%
1497291
0.1%
1495291
0.1%
1492851
0.1%
1491911
0.1%
1488461
0.1%
1488381
0.1%
1487641
0.1%
1487241
0.1%
1486421
0.1%

rating
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size39.8 KiB
1
143 
2
143 
4
142 
3
138 
5
134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1143
20.4%
2143
20.4%
4142
20.3%
3138
19.7%
5134
19.1%

Length

2022-02-28T03:49:02.610628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-28T03:49:02.636314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1143
20.4%
2143
20.4%
4142
20.3%
3138
19.7%
5134
19.1%

Most occurring characters

ValueCountFrequency (%)
1143
20.4%
2143
20.4%
4142
20.3%
3138
19.7%
5134
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1143
20.4%
2143
20.4%
4142
20.3%
3138
19.7%
5134
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1143
20.4%
2143
20.4%
4142
20.3%
3138
19.7%
5134
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1143
20.4%
2143
20.4%
4142
20.3%
3138
19.7%
5134
19.1%

condition
Real number (ℝ≥0)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.517142857
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.664879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.813414191
Coefficient of variation (CV)0.5099404281
Kurtosis-1.170546115
Mean5.517142857
Median Absolute Deviation (MAD)2
Skewness-0.01292055277
Sum3862
Variance7.915299407
MonotonicityNot monotonic
2022-02-28T03:49:02.697410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
683
11.9%
580
11.4%
976
10.9%
872
10.3%
371
10.1%
468
9.7%
167
9.6%
263
9.0%
1062
8.9%
758
8.3%
ValueCountFrequency (%)
167
9.6%
263
9.0%
371
10.1%
468
9.7%
580
11.4%
683
11.9%
758
8.3%
872
10.3%
976
10.9%
1062
8.9%
ValueCountFrequency (%)
1062
8.9%
976
10.9%
872
10.3%
758
8.3%
683
11.9%
580
11.4%
468
9.7%
371
10.1%
263
9.0%
167
9.6%

economy
Real number (ℝ≥0)

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.68
Minimum8
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.729767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q110
median12
Q314
95-th percentile15
Maximum15
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.232648882
Coefficient of variation (CV)0.1911514454
Kurtosis-1.155827948
Mean11.68
Median Absolute Deviation (MAD)2
Skewness-0.08509888849
Sum8176
Variance4.98472103
MonotonicityNot monotonic
2022-02-28T03:49:02.760730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1298
14.0%
1195
13.6%
1595
13.6%
1394
13.4%
1487
12.4%
1081
11.6%
980
11.4%
870
10.0%
ValueCountFrequency (%)
870
10.0%
980
11.4%
1081
11.6%
1195
13.6%
1298
14.0%
1394
13.4%
1487
12.4%
1595
13.6%
ValueCountFrequency (%)
1595
13.6%
1487
12.4%
1394
13.4%
1298
14.0%
1195
13.6%
1081
11.6%
980
11.4%
870
10.0%

top speed
Real number (ℝ≥0)

Distinct66
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.8628571
Minimum135
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.803860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile137
Q1150
median167
Q3184
95-th percentile197
Maximum200
Range65
Interquartile range (IQR)34

Descriptive statistics

Standard deviation19.40785921
Coefficient of variation (CV)0.1163102415
Kurtosis-1.245618669
Mean166.8628571
Median Absolute Deviation (MAD)17
Skewness0.05574507099
Sum116804
Variance376.664999
MonotonicityNot monotonic
2022-02-28T03:49:02.850872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15120
 
2.9%
14517
 
2.4%
17616
 
2.3%
15516
 
2.3%
14115
 
2.1%
17915
 
2.1%
18415
 
2.1%
14615
 
2.1%
13514
 
2.0%
18814
 
2.0%
Other values (56)543
77.6%
ValueCountFrequency (%)
13514
2.0%
13610
1.4%
13712
1.7%
1389
1.3%
1399
1.3%
1409
1.3%
14115
2.1%
14212
1.7%
1439
1.3%
14411
1.6%
ValueCountFrequency (%)
20012
1.7%
19910
1.4%
19811
1.6%
19713
1.9%
19611
1.6%
19511
1.6%
19411
1.6%
1939
1.3%
1928
1.1%
19111
1.6%

hp
Real number (ℝ≥0)

Distinct71
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.12857143
Minimum50
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:02.901175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile53
Q169
median85
Q3102
95-th percentile117
Maximum120
Range70
Interquartile range (IQR)33

Descriptive statistics

Standard deviation20.14143228
Coefficient of variation (CV)0.2366001442
Kurtosis-1.13776237
Mean85.12857143
Median Absolute Deviation (MAD)17
Skewness-0.04302777486
Sum59590
Variance405.6772941
MonotonicityNot monotonic
2022-02-28T03:49:02.949685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10722
 
3.1%
7418
 
2.6%
9517
 
2.4%
7717
 
2.4%
10617
 
2.4%
8214
 
2.0%
11814
 
2.0%
6414
 
2.0%
10114
 
2.0%
8414
 
2.0%
Other values (61)539
77.0%
ValueCountFrequency (%)
5010
1.4%
519
1.3%
5213
1.9%
5313
1.9%
5411
1.6%
559
1.3%
568
1.1%
578
1.1%
587
1.0%
5911
1.6%
ValueCountFrequency (%)
12010
1.4%
1198
1.1%
11814
2.0%
1177
1.0%
1165
 
0.7%
1158
1.1%
1146
0.9%
11310
1.4%
11210
1.4%
1119
1.3%

torque
Real number (ℝ≥0)

Distinct73
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.9942857
Minimum68
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:03.000514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile71
Q184.75
median105
Q3122
95-th percentile137
Maximum140
Range72
Interquartile range (IQR)37.25

Descriptive statistics

Standard deviation21.29827604
Coefficient of variation (CV)0.2048023687
Kurtosis-1.234102621
Mean103.9942857
Median Absolute Deviation (MAD)19
Skewness-0.04438639078
Sum72796
Variance453.6165624
MonotonicityNot monotonic
2022-02-28T03:49:03.047794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7519
 
2.7%
11418
 
2.6%
10017
 
2.4%
12517
 
2.4%
12016
 
2.3%
9515
 
2.1%
12115
 
2.1%
7915
 
2.1%
7113
 
1.9%
13513
 
1.9%
Other values (63)542
77.4%
ValueCountFrequency (%)
686
 
0.9%
6910
1.4%
709
1.3%
7113
1.9%
7211
1.6%
7313
1.9%
7411
1.6%
7519
2.7%
767
 
1.0%
778
1.1%
ValueCountFrequency (%)
1409
1.3%
1396
0.9%
13812
1.7%
1379
1.3%
13611
1.6%
13513
1.9%
1346
0.9%
13310
1.4%
13210
1.4%
1316
0.9%

current price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308575.925
Minimum28226.5
Maximum584267.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-02-28T03:49:03.097273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28226.5
5-th percentile113888.125
Q1208547.125
median306864
Q3411083
95-th percentile506901.25
Maximum584267.5
Range556041
Interquartile range (IQR)202535.875

Descriptive statistics

Standard deviation123607.8242
Coefficient of variation (CV)0.4005750746
Kurtosis-0.9074577715
Mean308575.925
Median Absolute Deviation (MAD)100106.75
Skewness0.05002269533
Sum216003147.5
Variance1.52788942 × 1010
MonotonicityNot monotonic
2022-02-28T03:49:03.142987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1465271
 
0.1%
4105161
 
0.1%
308643.51
 
0.1%
383570.51
 
0.1%
555336.51
 
0.1%
185485.51
 
0.1%
456827.51
 
0.1%
291815.51
 
0.1%
224533.51
 
0.1%
1462231
 
0.1%
Other values (690)690
98.6%
ValueCountFrequency (%)
28226.51
0.1%
46920.51
0.1%
530401
0.1%
559251
0.1%
583841
0.1%
69231.51
0.1%
708621
0.1%
713141
0.1%
743981
0.1%
767721
0.1%
ValueCountFrequency (%)
584267.51
0.1%
584116.51
0.1%
5823471
0.1%
5582531
0.1%
555336.51
0.1%
5539011
0.1%
5538551
0.1%
5494061
0.1%
5477461
0.1%
5473781
0.1%

Interactions

2022-02-28T03:48:57.508830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.567308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.614216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.657033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.701206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.747490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.794830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.840887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.887583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.933770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:57.981513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.028757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.136749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.180688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.225271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.268319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.313329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.357729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.402041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.446660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.490465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.532134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.574533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.612328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.650203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.687796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.728839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.771141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.810394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.850608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.890187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.931317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:58.972669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.009730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.048333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.085112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.123830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.223798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.263562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.302113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.341945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.384172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.426335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.464414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.503252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.541259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.583230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.621450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.660084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.698768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.737824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.782302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.826589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.867405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.907589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.947539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:48:59.989342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.031322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.074354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.116741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.158848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.202647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.307382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.348607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.389016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.429463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.471865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.513026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.554773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.595996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.636682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.681208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.726987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.770357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.812412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.853642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.896631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.940939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:00.985226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.027876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.070459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.116645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.162112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.204027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.246533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.286796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.329186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.432216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.474359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.516593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.558482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.601634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.644501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.682847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.721247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.759233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.804088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.847719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.899232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-28T03:49:01.941494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-28T03:49:03.246901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-28T03:49:03.323390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-28T03:49:03.386693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-28T03:49:03.450204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-28T03:49:02.007928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-28T03:49:02.083537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

on road oldon road nowyearskmratingconditioneconomytop speedhptorquecurrent price
05842967317865128071451217182119146527.0
156146985622521065063910174106120312703.0
26481988584192125519388185105109267953.5
3626056722367412636016141638887172415.0
4587586740933313763125141768187115462.5
5600224892785793335451320011589367122.0
66761707103746134933251118111273151474.0
756839583170561472975312156111116108921.5
868580689834737636348815211584503606.5
95022817593937619105410162113106377108.5

Last rows

on road oldon road nowyearskmratingconditioneconomytop speedhptorquecurrent price
690695631899083413771419131978385275781.0
691584015724269710579248151538695239746.0
692589508737952387920561518271127317681.5
6935871877773973109589231416493101244229.0
69452178570110871272291691756413498242.5
69558311381043566703656151675175430451.5
696522202828170396723421114311473288926.0
697692157824926714295924919811699179077.0
6986610277291236117727461320011674224958.5
699624535801702567075181513811075459914.5